Pattern recognition is becoming a key application in body-area networks. This paper presents a framework promoting unsupervised training for multi-modal, multi-sensor classification systems. Specifically, it enables sensors provided with pattern-recognition capabilities to autonomously supervise the learning process of other sensors. The approach is discussed using a case study combining a smart camera and a body-worn accelerometer. The body-worn accelerometer sensor is trained to recognize four user activities pairing accelerometer data with labels coming from the camera. Experimental results illustrate the applicability of the approach in different conditions. © 2010 ACM.
Unsupervised learning in body-area networks / Bicocchi, N.; Lasagni, M.; Mamei, M.; Prati, A.; Cucchiara, R.; Zambonelli, F.. - (2011), pp. 164-170. (Intervento presentato al convegno 5th International ICST Conference on Body Area Networks, BodyNets 2010 tenutosi a Corfu, grc nel 2010) [10.1145/2221924.2221955].
Unsupervised learning in body-area networks
Prati A.;
2011-01-01
Abstract
Pattern recognition is becoming a key application in body-area networks. This paper presents a framework promoting unsupervised training for multi-modal, multi-sensor classification systems. Specifically, it enables sensors provided with pattern-recognition capabilities to autonomously supervise the learning process of other sensors. The approach is discussed using a case study combining a smart camera and a body-worn accelerometer. The body-worn accelerometer sensor is trained to recognize four user activities pairing accelerometer data with labels coming from the camera. Experimental results illustrate the applicability of the approach in different conditions. © 2010 ACM.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
